Ensembling methods for countrywide short-term forecasting of gas demand

被引:0
作者
Marziali, Andrea [1 ]
Fabbiani, Emanuele [1 ]
De Nicolao, Giuseppe [1 ]
机构
[1] Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia, Italy
关键词
natural gas; time series forecasting; neural networks; statistical learning; ensemble methods; CONSUMPTION; REGRESSION; REGULARIZATION; PREDICTION; SELECTION;
D O I
10.1504/ijogct.2021.10035077
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Gas demand is made of three components: residential, industrial, and thermoelectric gas demand. Herein, the one-day-ahead prediction of each component is studied, using Italian data as a case study. Statistical properties and relationships with temperature are discussed, as a preliminary step for an effective feature selection. Nine 'base forecasters' are implemented and compared: ridge regression, gaussian processes, nearest neighbours, artificial neural networks, torus model, LASSO, elastic net, random forest, and support vector regression (SVR). Based on them, four ensemble predictors are crafted: simple average, weighted average, subset average, and SVR aggregation. We found that ensemble predictors perform consistently better than base ones. Moreover, our models outperformed transmission system operator (TSO) predictions in a two-year out-of-sample validation. Such results suggest that combining predictors may lead to significant performance improvements in gas demand forecasting. [Received: June 30, 2019; Accepted: September 29, 2019]
引用
收藏
页码:184 / 201
页数:18
相关论文
共 27 条
[1]  
Armstrong J.S, 2001, PRINCIPLES FORECASTI, V30
[2]   An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments [J].
Azadeh, A. ;
Asadzadeh, S. M. ;
Ghanbari, A. .
ENERGY POLICY, 2010, 38 (03) :1529-1536
[3]   Natural gas consumption forecasting for anomaly detection [J].
Baldacci, Lorenzo ;
Golfarelli, Matteo ;
Lombardi, Davide ;
Sami, Franco .
EXPERT SYSTEMS WITH APPLICATIONS, 2016, 62 :190-201
[4]   Analysis and future outlook of natural gas consumption in the Italian residential sector [J].
Bianco, Vincenzo ;
Scarpa, Federico ;
Tagliafico, Luca A. .
ENERGY CONVERSION AND MANAGEMENT, 2014, 87 :754-764
[5]   Scenario analysis of nonresidential natural gas consumption in Italy [J].
Bianco, Vincenzo ;
Scarpa, Federico ;
Tagliafico, Luca A. .
APPLIED ENERGY, 2014, 113 :392-403
[6]  
Breiman L., 2017, Classification and Regression Trees, DOI [DOI 10.1201/9781315139470, 10.1201/9781315139470/CLASSIFICATION-REGRESSION-TREES-LEO-BREIMAN-JEROME-FRIEDMAN-RICHARD-OLSHEN-CHARLES-STONE]
[7]  
De Nicolao G., 2019, ARXIV190102719
[8]  
Dujak D, 2017, 28 INT C CENTR EUR C
[9]   Regularization networks and support vector machines [J].
Evgeniou, T ;
Pontil, M ;
Poggio, T .
ADVANCES IN COMPUTATIONAL MATHEMATICS, 2000, 13 (01) :1-50
[10]   Generalized model of prediction of natural gas consumption [J].
Gil, S ;
Deferrari, J .
JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME, 2004, 126 (02) :90-98